This paper introduces the methods we adopt to build our system for the evaluation event of Voice Conversion Challenge (VCC) 2016. We propose to use neural network-based approaches to convert both spectral and excitation features. First, the generatively trained deep neural network (GTDNN) is adopted for spectral envelope conversion after the spectral envelopes have been pre-processed by frequency warping. Second, we propose to use a recurrent neural network (RNN) with long short-term memory (LSTM) cells for F0 trajectory conversion. In addition, we adopt a DNN for band aperiodicity conversion. Both internal tests and formal VCC evaluation results demonstrate the effectiveness of the proposed methods.